RRepoGEO

REPOGEO REPORT · LITE

explosion/thinc

Default branch v8.3.x · commit 6c38b299 · scanned 6/20/2026, 10:01:57 PM

GitHub: 2,889 stars · 292 forks

Scan history for this repo

Score trend below includes all ready runs (older left, newer right; scroll horizontally if needed). The table is collapsed by default—expand for newest-first rows, 10 per page.

Score trend (left → right: older → newer)

2 ready scans. Expand the table below for newest-first rows (10 per page, paginated).

AI VISIBILITY SCORE
40 /100
Critical
Category recall
0 / 2
Not recommended in any query
Rule findings
2 pass · 0 warn · 0 fail
Objective metadata checks
AI knows your name
3 / 3
Direct prompts that named your repo
HOW TO READ THIS REPORT

Action plan is what to do next — copy-pasteable changes prioritized by impact. Category visibility is the real GEO test: when a user asks an AI a brand-free question that should surface explosion/thinc, does the AI actually recommend you — or your competitors? Objective checks verify the metadata signals AI engines weight first. Self-mention check detects whether AI even knows you exist by name.

Action plan — copy-paste fixes

3 prioritized changes generated by gemini-2.5-flash. Mark items done after you ship the fix.

OVERALL DIRECTION
  • highreadme#1
    Reposition the README's first sentence to emphasize functional, type-safe model composition for production

    Why:

    CURRENT
    Thinc is a **lightweight deep learning library** that offers an elegant, type-checked, functional-programming API for **composing models**, with support for layers defined in other frameworks such as **PyTorch, TensorFlow and MXNet**.
    COPY-PASTE FIX
    Thinc is a **lightweight deep learning library** designed for **functional composition of deep learning models**, offering an elegant, **type-checked API** for **production systems**. It seamlessly integrates layers from frameworks like **PyTorch, TensorFlow, and MXNet**.
  • hightopics#2
    Add more specific topics to improve categorization for functional and type-safe deep learning

    Why:

    CURRENT
    ai, artificial-intelligence, deep-learning, functional-programming, jax, machine-learning, machine-learning-library, mxnet, natural-language-processing, nlp, python, pytorch, spacy, tensorflow, type-checking
    COPY-PASTE FIX
    ai, artificial-intelligence, deep-learning, functional-programming, jax, machine-learning, machine-learning-library, mxnet, natural-language-processing, nlp, python, pytorch, spacy, tensorflow, type-checking, model-composition, functional-deep-learning, type-safe-ai, production-ai, deep-learning-integration
  • mediumreadme#3
    Add a 'Why Thinc?' or 'Comparison' section to differentiate from general frameworks

    Why:

    COPY-PASTE FIX
    Add a new section (e.g., 'Why Thinc?' or 'Thinc vs. Other Frameworks') to the README. Include a sentence like: 'Unlike general-purpose frameworks such as PyTorch, TensorFlow, or JAX, Thinc focuses on providing a lightweight, type-checked, functional API for *composing* and *integrating* models from various backends into robust production systems.'

Category GEO backends resolved for this scan: google/gemini-2.5-flash, deepseek/deepseek-v4-flash

Category visibility — the real GEO test

Brand-free queries asked to google/gemini-2.5-flash. Did AI recommend you, or someone else?

Same questions for every model — switch tabs to compare answers and rankings.

Recall
0 / 2
0% of queries surface explosion/thinc
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
PyTorch
Recommended in 2 of 2 queries
COMPETITOR LEADERBOARD
  1. PyTorch · recommended 2×
  2. JAX · recommended 2×
  3. TensorFlow · recommended 2×
  4. Keras Functional API · recommended 1×
  5. flax.linen · recommended 1×
  • CATEGORY QUERY
    Looking for a lightweight Python library to functionally compose deep learning models.
    you: not recommended
    AI recommended (in order):
    1. Keras Functional API
    2. PyTorch
    3. JAX
    4. flax.linen
    5. TensorFlow
    6. Haiku
    7. NNX

    AI recommended 7 alternatives but never named explosion/thinc. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    Need a deep learning framework providing type-safe model definitions for production systems.
    you: not recommended
    AI recommended (in order):
    1. JAX
    2. Flax
    3. Equinox
    4. PyTorch
    5. MyPy
    6. TensorFlow
    7. Keras
    8. MXNet
    9. ONNX Runtime
    10. ONNX

    AI recommended 10 alternatives but never named explosion/thinc. This is the gap to close.

    Show full AI answer

Objective checks

Rule-based audits of metadata signals AI engines weight most.

  • Metadata completeness
    pass

  • README presence
    pass

Self-mention check

Does AI even know your repo exists when asked about it directly?

  • Compared to common alternatives in this category, what is the core differentiator of explosion/thinc?
    pass
    AI named explosion/thinc explicitly

    AI answers can be confidently wrong. Read for accuracy: does it match your actual tech stack, audience, and differentiator?

  • If a team adopts explosion/thinc in production, what risks or prerequisites should they evaluate first?
    pass
    AI named explosion/thinc explicitly

    AI answers can be confidently wrong. Read for accuracy: does it match your actual tech stack, audience, and differentiator?

  • In one sentence, what problem does the repo explosion/thinc solve, and who is the primary audience?
    pass
    AI named explosion/thinc explicitly

    AI answers can be confidently wrong. Read for accuracy: does it match your actual tech stack, audience, and differentiator?

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explosion/thinc — Lite scans stay free; this card itemizes Pro deep limits vs Lite.

  • Deep reports10 / month
  • Brand-free category queries5 vs 2 in Lite
  • Prioritized action items8 vs 3 in Lite